This invention is in the field of data communications, and is more specifically directed to redundant coding for error detection and correction in such communications.
High-speed data communications, for example in providing high-speed Internet access, is now a widespread utility for many businesses, schools, and homes. In its current stage of development, this access is provided according to an array of technologies. Data communications are carried out over existing telephone lines, with relatively slow data rates provided by voice band modems (e.g., according to the current v.92 communications standards), and higher data rates provided by Digital Subscriber Line (DSL) technology. Another current technology involves the use of cable modems communicating over coaxial cable, often in combination with cable television services. The Integrated Services Digital Network (ISDN) is a system of digital phone connections over which data is transmitted simultaneously across the world using end-to-end digital connectivity. Localized wireless network connectivity according to the IEEE 802.11 standard has become popular for connecting computer workstations and portable computers to a local area network (LAN), and often through the LAN to the Internet. Wireless data communication in the Wide Area Network (WAN) context, which provides cellular-type connectivity for portable and handheld computing devices, is now also becoming a popular technology.
A problem that is common to all data communications technologies is the corruption of data by noise. As is fundamental in the art, the signal-to-noise ratio for a communications channel is a degree of goodness of the communications carried out over that channel, as it conveys the relative strength of the signal that carries the data (as attenuated over distance and time), to the noise present on that channel. These factors relate directly to the likelihood that a data bit or symbol as received is in error relative to the data bit or symbol as transmitted. This likelihood is reflected by the error probability for the communications over the channel, commonly expressed as the Bit Error Rate (BER) ratio of errored bits to total bits transmitted. In short, the likelihood of error in data communications must be considered in developing a communications technology. Techniques for detecting and correcting errors in the communicated data must be incorporated for the communications technology to be useful.
Error detection and correction techniques are typically implemented by the technique of redundant coding. In general, redundant coding inserts data bits into the transmitted data stream that do not add any additional information, but that indicate, on decoding, whether an error is present in the received data stream. More complex codes provide the ability to deduce the true transmitted data from a received data stream even if errors are present.
Many types of redundant codes that provide error correction have been developed. One type of code simply repeats the transmission, for example repeating the payload twice, so that the receiver deduces the transmitted data by applying a decoder that determines the majority vote of the three transmissions for each bit. Of course, this simple redundant approach does not necessarily correct every error, but greatly reduces the payload data rate. In this example, a predictable likelihood remains that two of three bits are in error, resulting in an erroneous majority vote despite the useful data rate having been reduced to one-third. More efficient approaches, such as Hamming codes, have been developed toward the goal of reducing the error rate while maximizing the data rate.
The well-known Shannon limit provides a theoretical bound on the optimization of decoder error as a function of data rate. The Shannon limit provides a metric against which codes can be compared, both in the absolute and relative to one another. Since the time of the Shannon proof, modem data correction codes have been developed to more closely approach the theoretical limit. An important type of these conventional codes are “turbo” codes, which encode the data stream by applying two convolutional encoders. One of these convolutional encoders encodes the datastream as given, while the other encodes a pseudo-randomly interleaved version of the data stream. The results from the two encoders are interwoven to produce the encoded data stream.
Another class of known redundant codes are the Low Density Parity Check (LDPC) codes. The fundamental paper describing these codes is Gallager, Low-Density Parity-Check Codes, (MIT Press, 1963), monograph available at http://www.inference.phy.cam.ac.uk/mackay/gallager/papers/. In these codes, a sparse matrix H defines the code, with the encodings c of the payload data satisfying:Hc=0  (1)over Galois field GF(2). Each encoding c consists of the source message ci combined with the corresponding parity check bits cp for that source message ci. The encodings c are transmitted, with the receiving network element receiving a signal vector r=c+n, n being the noise added by the channel. Because the decoder at the receiver knows matrix H, it can compute a vector z=Hr. However, because r=c+n, and because Hc=0:z=Hr=Hc+Hn=Hn  (2)The decoding process thus involves finding the sparsest vector x that satisfies the equation:Hx=z  (3)over GF(2). The vector x becomes the best guess for noise vector n, which can be subtracted from the received signal vector r to recover encodings c, from which the original source message ci is recoverable.
There are many known implementations of LDPC codes. Some of these LDPC codes have been described as providing code performance that approaches the Shannon limit, as described in MacKay et al., “Comparison of Constructions of Irregular Gallager Codes”, Trans. Comm., Vol. 47, No. 10 (IEEE, October 1999), pp. 1449–54, and in Tanner et al., “A Class of Group-Structured LDPC Codes”, ISTCA-2001 Proc. (Ambleside, England, 2001).
In theory, the encoding of data words according to an LDPC code is straightforward. Given enough memory or small enough data words, one can store all possible code words in a lookup table, and look up the code word in the table according to the data word to be transmitted. But modern data words to be encoded are on the order of 1 kbits and larger, rendering lookup tables prohibitively large. Accordingly, algorithms have been developed that derive codewords, in real time, from the data words to be transmitted. A straightforward approach for generating a codeword is to consider the n-bit codeword vector c in its systematic form, having data or information portion ci and an m-bit parity portion cp such that c=(ci, cp). Similarly, parity matrix H is placed into a systematic form Hsys, preferably in a lower triangular form for the m parity bits. In this conventional encoder, the information portion ci is filled with n-m information bits, and the m parity bits are derived by back-substitution with the systematic parity matrix Hsys. This approach is described in Richardson and Urbanke, “Efficient Encoding of Low-Density Parity-Check Codes”, IEEE Trans. on Information Theory, Vol. 47, No. 2 (February 2001), pp. 638–656. This article indicates that, through matrix manipulation, the encoding of LDPC codewords can be accomplished in a number of operations that approaches a linear relationship with the size n of the codewords. However, the computational efficiency in this and other conventional LDPC encoding techniques does not necessarily translate into an efficient encoder hardware architecture. Specifically, these and other conventional encoder architectures are inefficient because the typically involve the storing of inverse matrices, by way of which the parity check equation (1) or a corollary is solved in the encoding operation.
By way of further background, my copending patent application Ser. No. 10/329,597, filed Dec. 26, 2002, commonly assigned herewith, and incorporated herein by this reference, describes a family of structured irregular LDPC codes, and a decoding architecture for those codes. It has been discovered, in connection with this invention, that these structured LDPC codes can also provide efficiencies in the hardware implementation of the encoder.